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Rows, columns, and consequences

Speak at Rootconf’s Special Edition on Databases

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Arpit Bhayani

Arpit Bhayani

@arpit_bhayani

Databases Were Not Designed For This

Submitted Apr 1, 2026

Session type - select the format for your session: 30-minute talk – technical deep dive

Description

Databases were not designed for agents. They were built around a set of implicit assumptions: callers issue predictable queries, connections are short-lived, bad queries fail loudly, and schemas are a contract with engineers. Agentic systems break every one of these assumptions. Agents reason their way to queries, hold connections while an LLM thinks, retry operations unpredictably, and read your schema as natural language -- so a column named flg_1 is a bug, not a style choice.

The session walks through each broken assumption and the concrete fix for it. Role-level timeouts, per-agent database roles with minimum privilege, soft deletes with agent identity tracking, append-only event logs with idempotency key constraints, and query tagging. Every fix is a SQL or Python snippet you can take back and apply the same week.

Takeaways

  1. Your database is not broken -- your assumptions are. Agents expose implicit contracts baked into traditional schema design, connection pooling, and query monitoring that were never written down anywhere.

  2. A defensive data layer is not optional for agentic systems. Idempotency keys, append-only logs, and per-agent roles are the difference between a recoverable mistake and silent data corruption.

Target Audience

Backend and platform engineers who are integrating LLM agents into production systems or are about to. Also useful for database administrators and engineering leads making architectural decisions about agentic workloads, or those who want to improve the robustness and observability posture of their databases.

Bio

Arpit Bhayani is a Principal Engineer II at Razorpay, where he is working at the intersection of Data and AI. In the past, he was India Tech Lead for GCP Memorystore (providing managed Redis to GCP customers) and GCP Dataproc (providing managed Spark ecosystem to GCP customers).

He writes and teaches about database internals, system design, and engineering fundamentals at arpitbhayani.me, and shares content with a large engineering audience on YouTube, LinkedIn, and Twitter.

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